Summary
Finding where a signal is coming from in three dimensions—both its left-right and up-down angles—is important for many technologies like radar and wireless communication. This paper presents a new way to arrange antennas in two circular groups with special numbers of antennas that work well together, all sharing the same circle size. The authors designed a method that avoids complicated math tricks used before and instead uses a quick two-step approach: first a rough search, then a smart fine-tuning to get very accurate direction information. Their method reduces errors and works better even when the signals are weak, and it matches the best possible accuracy predicted by theory at high signal strengths.
Direction-of-Arrival (DoA) estimationCo-prime arraysUniform circular arrayAzimuth angleElevation angleSignal-to-noise ratio (SNR)Cramer-Rao BoundSwarm intelligenceMutual couplingPhase ambiguity
Abstract
This paper proposes a shared-radius co-prime circular array for high-resolution, continuous 2D Direction-of-Arrival (DoA) estimation in 3D space, jointly estimating azimuth and elevation angles. The proposed architecture consists of two uniform circular sub-arrays with co-prime antenna counts sharing a common radius RR, a design that intrinsically suppresses mutual coupling leakage compared to dense uniform arrays. Unlike existing works that rely on complex phase-mode transformations to map circular structures to virtual linear arrays, we introduce a hybrid continuous-recovery framework operating directly in the physical spatial domain. By integrating a fast, discrete coarse-grid search with a swarm-intelligence continuous refinement stage, the proposed method completely bypasses discrete grid-mismatch limitations and computationally expensive eigenvalue decompositions. A rigorous theoretical analysis using Nivens Theorem establishes the spatial uniqueness of the true source direction, effectively resolving phase ambiguities. Simulation results demonstrate that this hybrid scheme achieves superior resolution and lower Root Mean Square Error (RMSE) at low Signal-to-Noise Ratios (SNR) compared to uniform configurations, while asymptotically converging to the theoretical Cramer-Rao Bound (CRB) at high SNRs.